In the past, it has been performed to recognize letters in an object image by comparing the object image with a reference image stored in a memory. For example, as disclosed in Japanese Patent Application [kokai] No. 8-212290, there is a method of identifying letters included in an original image, which comprises the steps of binarizing the letters to be identified, performing a normalization treatment to the obtained binary image, and inputting the normalized data into a neural network. According to this method, it is possible to accurately identify letters and/or numerical characters even from a number plate of a moving automobile.
However, in this method, there is a case that accurate recognition results can not be obtained when the original image includes some noises and/or blur. In particular, when a quality of the original image is relatively low, for example, the original image includes some characters such as numerical characters, each of which is composed of a plurality of elements, and/or undesired dots around the characters in the background, as shown in FIG. 1A, there is a fear that time needed to recognize the characters considerably increases despite a decrease in recognition accuracy.